Statically identifying XSS using deep learning
نویسندگان
چکیده
Cross-site Scripting (XSS) is ranked first in the top 25 Most Dangerous Software Weaknesses (2020) of Common Weakness Enumeration (CWE) and places this vulnerability as most dangerous among programming errors. This work explores static approaches to detect XSS vulnerabilities using neural networks. We compare two different code representations based on Natural Language Processing (NLP) Programming (PLP) experiment with models network architectures for analysis detection PHP Node.js. train evaluate synthetic databases. Using generated Node.js databases, we our results three well-known analyzers code, ProgPilot, Pixy, RIPS, a known scanner Node.js, AppScan mode. Our networks overperform existing tools all cases.
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ژورنال
عنوان ژورنال: Science of Computer Programming
سال: 2022
ISSN: ['1872-7964', '0167-6423']
DOI: https://doi.org/10.1016/j.scico.2022.102810